In the field of radio communications, signal transmission requires the use of power amplifiers, essential in transmission chains. The power amplifiers used in radio communications are generally non-linear; non-linearity phenomena are all the more present in that current power amplifiers operate in areas close to their saturation, with a view to optimizing their performance, input dynamics are significant, and variable envelope signals are increasingly used. The non-linear behaviour of power amplifiers notably generates phase and amplitude distortions on the transmitted signals, which notably generate spectral feedback outside the useful signal channel. Such spectral feedback is undesirable: first, the requirements demanded from radio communication devices, in terms of spectral efficiency, are increasingly severe, with the increasing variety of wireless communication devices. Standards define precise requirements in this regard. Secondly, spectral feedback has a negative influence on the correct operation of devices situated close to the system including a power amplifier. This is because a vehicle, for example, may be equipped with a large number of systems operating in relatively similar frequency ranges. Distortions may, for example, be characterized by the ratio between the power of the signal in the useful channel and the power of the signal generated by the distortions in the adjacent channels, this ratio being commonly referred to by the English abbreviation ACPR (“Adjacent Channel Power Ratio”); other characteristic values may also be used, such as the magnitude of the error vector, commonly referred to by the English abbreviation EVM (“Error Vector Magnitude”).
Linearization of power amplifiers is a favoured solution for reducing non-linear distortion phenomena in the transmission chains of transmitters and increasing their performance. There are various techniques of linearizing power amplifiers known from the prior art. Among the various known techniques of linearization, adaptive digital baseband predistortion is one of the most efficient in terms of cost-effectiveness, thanks to digital implementation, offering accuracy and flexibility. This technique can be used to obtain very good linearity performances, which can be achieved with better power yields, as well as reduced complexity and cost compared with existing analogue techniques. Generally, predistortion techniques consist in transforming the signals upstream from the power amplifier, in order for the combination with the power amplifier to make the overall system linear. Thus, if a circuit performs this transformation, a perfect linearization is theoretically achievable, by placing this upstream from the power amplifier. Such a circuit is described as “pre-inverse” and is commonly called a “precompensator” or “predistorter”.
There are various techniques for producing adaptive digital baseband predistortion. Each of them consists in transposing the radio frequency transmission signal at the power amplifier output into the baseband and digitizing its in-phase and quadrature components using an analogue-to-digital converter. The baseband samples are then processed in a special digital processor, with an identification algorithm which compares them with the samples corresponding to the reference input signal. The process of identifying the parameters of the precompensator is performed digitally and seeks to minimize the error between the power amplifier input and output. After a characteristic convergence time of the identification algorithm, the precompensator may operate as the exact pre-inverse of the equivalent baseband model of the power amplifier. The algorithm may, for example, be implemented in a specific integrated circuit of the “ASIC” type, whose English acronym corresponds to “Application-Specific Integrated Circuit”, or in an “FPGA” (“Field Programmable Gate Array”) type programmable circuit, or yet again in a digital processing processor, commonly referred to as a “DSP” (“Digital Signal Processor”) in English.
More particularly, among the known techniques of adaptive digital baseband predistortion, two categories may be mentioned, based on two approaches to implementing the precompensator:                implementation using correspondence tables or “TC”, stored in memories commonly referred to as “LUT” (“Look-Up Table”) in English. This category is particularly suited to amplifiers whose memory effects are negligible.        implementation using parametric models. The range of parametric models in this type of application is vast: parametric models may be simple memoryless polynomial models, up to distinctly more complex models, such as Volterra series models and neural network models.        
The precompensator is, for example, a digital processor which processes the complex envelope of the input signal, generally represented by its in-phase and quadrature components designated respectively by the letters I and Q, and sampled at a determined frequency; thus, the sample occurring at the precompensator input at a given instant is a complex value. In order to simultaneously correct the amplitude and phase distortions of the power amplifier, the amplitude and phase, or the real and imaginary parts of each complex value corresponding to an input sample, are adjusted.
The correspondence table technique offers the advantage of being simple to implement; however, this technique has the drawback of the relatively long convergence time. Furthermore, the use of large dimension tables may be required, necessitating storage memories that may be difficult to implement.
The implementation of parametric models considerably increases the complexity of the system and may cause problems of instability when the orders of polynomials employed are high, or even problems of non-convergence of algorithms.